Decentralized stochastic optimization based planning of integrated transmission and distribution networks with distributed generation penetration

Abstract In a current power system, numbers of distribution networks are physically connected to a transmission network at different boundary buses. As the planning solution of one network significantly influences the decisions made by planners of other networks, the transmission and distribution networks should coordinate and cooperate with each other to design the entire power system in a secure and economic manner. Inspired by decentralized and hierarchical optimization theories, this paper proposes a coordinated decision-making framework to determine the planning scheme and scenario based generation schedule for integrated transmission and distribution networks (ITDNs) with the penetration of distributed generations (DGs). A stochastic bi-level hierarchy is presented to decompose the centralized optimal planning of ITDNs. The obtained subproblems for independent transmission and distribution networks are formulated and relaxed to convex models. An improved iterative solution procedure is developed by exploiting the cascaded structure of the problems. Theoretical analysis and numerical results demonstrate the convergence properties of the decentralized optimization algorithm. The proposed coordinated planning framework outperforms conventional independent methods by decreasing expansion investment and improving DG accommodation.

[1]  Yong Fu,et al.  System of Systems Based Security-Constrained Unit Commitment Incorporating Active Distribution Grids , 2014, IEEE Transactions on Power Systems.

[2]  Steven H. Low,et al.  Branch Flow Model: Relaxations and Convexification—Part II , 2012 .

[3]  Ross Baldick,et al.  Coarse-grained distributed optimal power flow , 1997 .

[4]  Jovica V. Milanovic,et al.  Validation of Equivalent Dynamic Model of Active Distribution Network Cell , 2013, IEEE Transactions on Power Systems.

[5]  Nima Amjady,et al.  Generation and Transmission Expansion Planning: MILP–Based Probabilistic Model , 2014, IEEE Transactions on Power Systems.

[6]  John R. Birge,et al.  An Improved Stochastic Unit Commitment Formulation to Accommodate Wind Uncertainty , 2016, IEEE Transactions on Power Systems.

[7]  Abbas Khosravi,et al.  A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources , 2015 .

[8]  M. Rider,et al.  Imposing Radiality Constraints in Distribution System Optimization Problems , 2012 .

[9]  Omar J. Guerra,et al.  An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems , 2016 .

[10]  Hakan Ergun,et al.  Technology and Topology Optimization for Multizonal Transmission Systems , 2014, IEEE Transactions on Power Systems.

[11]  S. Low,et al.  Zero Duality Gap in Optimal Power Flow Problem , 2012, IEEE Transactions on Power Systems.

[12]  F. S. Hover,et al.  Convex Models of Distribution System Reconfiguration , 2012, IEEE Transactions on Power Systems.

[13]  M. Shahidehpour,et al.  Microgrid-Based Co-Optimization of Generation and Transmission Planning in Power Systems , 2013, IEEE Transactions on Power Systems.

[14]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[15]  Yong Fu,et al.  Chance-Constrained System of Systems Based Operation of Power Systems , 2016, IEEE Transactions on Power Systems.

[16]  Tao Xu,et al.  Hierarchical Risk Assessment of Transmission System Considering the Influence of Active Distribution Network , 2015, IEEE Transactions on Power Systems.

[17]  Wei Gu,et al.  Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM) , 2018 .

[18]  Anil Pahwa,et al.  Stochastic Networked Microgrid Energy Management With Correlated Wind Generators , 2017, IEEE Transactions on Power Systems.

[19]  Qixin Chen,et al.  Analysis of transmission expansion planning considering consumption-based carbon emission accounting , 2017 .

[20]  Zhaohong Bie,et al.  Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding , 2018 .

[21]  Vinod Khadkikar,et al.  Planning active distribution networks considering multi-DG configurations , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[22]  Carmen L. T. Borges,et al.  Multistage expansion planning for active distribution networks under demand and Distributed Generation uncertainties , 2012 .

[23]  Jamshid Aghaei,et al.  Integrated day-ahead and hour-ahead operation model of discos in retail electricity markets considering DGs and CO2 emission penalty cost , 2012 .

[24]  R. A. Jabr,et al.  Robust Transmission Network Expansion Planning With Uncertain Renewable Generation and Loads , 2013, IEEE Transactions on Power Systems.

[25]  L. Wehenkel,et al.  Sensitivity-Based Approaches for Handling Discrete Variables in Optimal Power Flow Computations , 2010, IEEE Transactions on Power Systems.

[26]  K. Ravindra,et al.  Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation , 2013, IEEE Transactions on Power Systems.

[27]  Panos Y. Papalambros,et al.  Convergence properties of analytical target cascading , 2002 .

[28]  Jay H. Lee,et al.  Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty , 2017 .

[29]  J. E. Rooda,et al.  An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers , 2006 .

[30]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[31]  Tomaso Erseghe,et al.  Distributed Optimal Power Flow Using ADMM , 2014, IEEE Transactions on Power Systems.

[32]  Hongbin Sun,et al.  Profit-seeking energy-intensive enterprises participating in power system scheduling: Model and mechanism , 2015 .

[33]  Arne Olson,et al.  Halfway There: Can California Achieve a 50% Renewable Grid? , 2015, IEEE Power and Energy Magazine.

[34]  Yurui Fan,et al.  Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China , 2018 .

[35]  Steven H. Low,et al.  Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence , 2014, IEEE Transactions on Control of Network Systems.

[36]  Henrik Sandberg,et al.  A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems , 2017, IEEE Transactions on Smart Grid.

[37]  Qinglai Guo,et al.  Coordinated Economic Dispatch of Coupled Transmission and Distribution Systems Using Heterogeneous Decomposition , 2016 .

[38]  Haozhong Cheng,et al.  Active distribution network expansion planning integrating dispersed energy storage systems , 2016 .

[39]  A. Conejo,et al.  Multi-area coordinated decentralized DC optimal power flow , 1998 .

[40]  Victor M. Zavala,et al.  Large-scale optimal control of interconnected natural gas and electrical transmission systems , 2016 .

[41]  Michael C. Georgiadis,et al.  A two-stage stochastic programming model for the optimal design of distributed energy systems , 2013 .

[42]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[43]  Mario Paolone,et al.  Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support , 2014, IEEE Transactions on Power Systems.

[44]  Peng Li,et al.  Optimal siting and sizing of soft open points in active electrical distribution networks , 2017 .

[45]  Zita Vale,et al.  Coordination between mid-term maintenance outage decisions and short-term security-constrained scheduling in smart distribution systems , 2012 .

[46]  Xiandong Xu,et al.  Hierarchical microgrid energy management in an office building , 2017 .

[47]  Michael C. Caramanis,et al.  A linear programming model for power distribution with demand response and variable renewable energy , 2016 .